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Building Computer Vision Applications Using Artificial Neural Networks: With Examples in OpenCV and TensorFlow with Python [Paperback] Ansari, Shamshad

Building Computer Vision Applications Using Artificial Neural Networks: With Examples in OpenCV and TensorFlow with Python [Paperback] Ansari, Shamshad

Building Computer Vision Applications Using Artificial Neural Networks: With
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Building Computer Vision Applications Using Artificial Neural Networks: With Examples in OpenCV and TensorFlow with Python [Paperback] Ansari, Shamshad Paperback - 2023

by Ingram

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  • Title Building Computer Vision Applications Using Artificial Neural Networks: With Examples in OpenCV and TensorFlow with Python [Paperback] Ansari, Shamshad
  • Author Ingram
  • Binding Paperback
  • Condition New
  • Pages 526
  • Volumes 1
  • Language ENG
  • Publisher Apress
  • Publication date 2023-11-18
  • Illustrated Yes
  • Features Illustrated
  • Bookseller's Inventory # OTF-S-9781484298657
  • ISBN 9781484298657 / 1484298659
  • Weight 2.08 lbs (0.94 kg)
  • Dimensions 10 x 7 x 1.11 in (25.40 x 17.78 x 2.82 cm)
  • Category Computers - General Information
  • Quantity available 91

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From the publisher

Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition's publication. All code used in the book has also been fully updated.

This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you'll gain a thorough understanding of them. The book's source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python.

Upon completing this book, you'll have the knowledge and skills to build your own computer vision applications using neural networks

What You Will Learn

  • Understand image processing, manipulation techniques, and feature extractionmethods
  • Work with convolutional neural networks (CNN), single-shot detector (SSD), and YOLO
  • Utilize large scale model development and cloud infrastructure deployment
  • Gain an overview of FaceNet neural network architecture and develop a facial recognition system

Who This Book Is For

Those who possess a solid understanding of Python programming and wish to gain an understanding of computer vision and machine learning. It will prove beneficial to data scientists, deep learning experts, and students.

From the rear cover

Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition's publication. All code used in the book has also been fully updated.

This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you'll gain a thorough understanding of them. The book's source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python.

Upon completing this book, you'll have the knowledge and skills to build your own computer vision applications using neural networks

You will:

  • Understand image processing, manipulation techniques, and feature extraction methods
  • Work with convolutional neural networks (CNN), single-shot detector (SSD), and YOLO
  • Utilize large scale model development and cloud infrastructure deployment
  • Gain an overview of FaceNet neural network architecture and develop a facial recognition system

About the author

Shamshad (Sam) Ansari is an author, inventor, and thought leader in the fields of computer vision, machine learning, artificial intelligence, and cognitive science. He has extensive experience in high scale, distributed, and parallel computing. Sam currently serves as an Adjunct Professor at George Mason University, teaching graduate- level programs within the Data Analytics Engineering department of the Volgenau School of Engineering. His areas of instruction encompass machine learning, natural language processing, and computer vision, where he imparts his knowledge and expertise to aspiring professionals.

Having authored multiple publications on topics such as machine learning, RFID, and high-scale enterprise computing, Sam's contributions extend beyond academia. He holds four US patents related to healthcare AI, showcasing his innovative mindset and practical application of technology.

Throughout his extensive 20+ years of experience in enterprise softwaredevelopment, Sam has been involved with several tech startups and early-stage companies. He has played pivotal roles in building and expanding tech teams from the ground up, contributing to their eventual acquisition by larger organizations. At the beginning of his career, he worked with esteemed institutions such as the US Department of Defense (DOD) and IBM, honing his skills and knowledge in the industry.

Currently, Sam serves as the President and CEO of Accure, Inc., an AI company that he founded. He is the creator, architect, and a significant contributor to Momentum AI, a no-code platform that encompasses data engineering, machine learning, AI, MLOps, data warehousing, and business intelligence. Throughout his career, Sam has made notable contributions in various domains including healthcare, retail, supply chain, banking and finance, and manufacturing. Demonstrating his leadership skills, he has successfully managed teams of software engineers, data scientists, and DevSecOps professionals, leading them to deliver exceptional results. Sam earned his bachelor's degree in engineering from Birsa Institute of Technology (BIT) Sindri and subsequently a Master's degree from the prestigious Indian Institute of Information Technology and Management Kerala (IIITM-K).

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